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Graphical comparisons of relative disease burden across multiple risk factors.
BMC Medical Research Methodology ( IF 4 ) Pub Date : 2019-09-11 , DOI: 10.1186/s12874-019-0827-4
John Ferguson 1 , Neil O'Leary 1 , Fabrizio Maturo 1 , Salim Yusuf 2 , Martin O'Donnell 1
Affiliation  

BACKGROUND Population attributable fractions (PAF) measure the proportion of disease prevalence that would be avoided in a hypothetical population, similar to the population of interest, but where a particular risk factor is eliminated. They are extensively used in epidemiology to quantify and compare disease burden due to various risk factors, and directly influence public policy regarding possible health interventions. In contrast to individual specific metrics such as relative risks and odds ratios, attributable fractions depend jointly on both risk factor prevalence and relative risk. The relative contributions of these two components is important, and usually needs to be presented in summary tables that are presented together with the attributable fraction calculation. However, representing PAF in an accessible graphical format, that captures both prevalence and relative risk, may assist interpretation. METHODS Taylor-series approximations to PAF in terms of risk factor prevalence and log-odds ratio are derived that facilitate simultaneous representation of PAF, risk factor prevalence and risk-factor/disease log-odds ratios on a single co-ordinate axis. Methods are developed for binary, multi-category and continuous exposure variables. RESULTS The methods are demonstrated using INTERSTROKE, a large international case control dataset focused on risk factors for stroke. CONCLUSIONS The described methods could be used as a complement to tables summarizing prevalence, odds ratios and PAF, and may convey the same information in a more intuitive and visually appealing manner. The suggested nomogram can also be used to visually estimate the effects of health interventions which only partially reduce risk factor prevalence. Finally, in the binary risk factor case, the approximations can also be used to quickly convert logistic regression coefficients for a risk factor into approximate PAFs.

中文翻译:

跨多个风险因素的相对疾病负担的图形比较。

背景技术人口归因分数(PAF)衡量在假设人群中与所关注人群相似但要消除特定风险因素的情况下可以避免的疾病患病率。它们被广泛用于流行病学中,以量化和比较由于各种风险因素引起的疾病负担,并直接影响有关可能的健康干预措施的公共政策。与个别特定指标(例如相对风险和优势比)相比,可归属分数共同取决于风险因素的普遍性和相对风险。这两个组成部分的相对贡献很重要,通常需要在汇总表中列出,并与归因分数计算一起列出。但是,以可访问的图形格式表示PAF,能够同时捕捉患病率和相对风险,可能有助于解释。方法根据危险因素患病率和对数奇数比得出泰勒级数对PAF的近似值,这有助于在单个坐标轴上同时表示PAF,危险因素患病率和危险因素/疾病对数奇数比。开发了用于二元,多类别和连续暴露变量的方法。结果使用INTERSTROKE演示了该方法,INTERSTROKE是一个大型国际病例对照数据集,重点关注中风的危险因素。结论所描述的方法可以用作汇总患病率,优势比和PAF的表格的补充,并且可以以更直观,更吸引人的方式传达相同的信息。建议的列线图还可以用于直观地评估健康干预措施的效果,这些措施只能部分降低风险因素的患病率。最后,在二元风险因素的情况下,也可以使用近似值将风险因素的逻辑回归系数快速转换为近似PAF。
更新日期:2019-09-11
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